Deep-learning network for automated evaluation of root-canal filling radiographic quality
The YOLOv5 deep learning network provided clinicians with a new, relatively accurate and efficient auxiliary tool for assessing the radiological quality of root canal fillings, enhancing work efficiency with large sample sizes. However, its use should be complemented by clinical expertise for accurate evaluations.
Authors
- Xueyang Zhang (ORCID: https://orcid.org/0000-0003-3625-219X)
- Xiaohui Rausch‐Fan (ORCID: https://orcid.org/0000-0002-8010-1914)
- Ze Zhang (ORCID: https://orcid.org/0000-0001-6335-6491)
- Fei Hu (ORCID: https://orcid.org/0000-0003-4346-9477)
- Bingran Du (ORCID: https://orcid.org/0000-0002-9028-8145)
- Zineng Xu
- Hailong Bai (ORCID: https://orcid.org/0000-0003-3488-4492)
- Yuan Lin (ORCID: https://orcid.org/0000-0003-3410-3588)
- Peng Ding (ORCID: https://orcid.org/0000-0003-4419-4858)
- Zhiwen Li
- Yaopeng Pan
- Liuli Jin
Institutions
- The First People's Hospital of Shunde (CN)
- University Clinic of Traumatology (AT)
- MemorialCare Health System (US)
- Southern Medical University (CN)
- Medical University of Vienna (AT)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1186/s40001-025-02331-x
- Citations
- 6
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.74
Funders
- Southern Medical University